Jessica Robin

A hallmark feature of episodic memory is the ability to flexibly recombine information across episodes to form new associations and guide behavior. This process, termed associative inference, relies on the hippocampus and surrounding medial temporal lobe (MTL) subregions. We previously found that cross-episode binding was improved when episodes were linked by scenes rather than by faces or objects. Here we tested whether differential recruitment of category-sensitive MTL subregions underlies these behavioral differences. Participants completed study-test phases of the Associative Inference in Memory (AIM) task, while undergoing fMRI scanning. During the study phase, they encoded overlapping AB and BC pairs. A and C items were always objects. The linking B item was either a face or a scene. At test, memory for the direct (AB, BC) and indirect associations (inferred AC) was tested. Category-sensitivity in MTL subregions was tested using an independent functional localizer, and the low integration (AB) trials from the study phase of the AIM task. Within the MTL, no subregions exhibited face-sensitivity. The anterior hippocampal head, anterolateral and posteromedial entorhinal cortex (alERC and pmERC), and parahippocampal cortex (PHC) were identified as scene-sensitive. Although accuracy of the indirect inferences did not differ between pairs linked by faces and scenes, MTL subregion recruitment differed across categories. Scene-sensitive subregions in the MTL cortex (alERC, pmERC, and PHC), but not the hippocampus (anterior hippocampal head), were recruited to support associative inference for faces during encoding. These findings suggest that regions in the MTL cortex identified as scene-sensitive here may be involved in integrating disparate elements of episodes into coherent representations, and may be recruited for non-scene stimuli when integration demands during encoding are high (e.g., during associative inference).

There is increasing interest in using computational speech and language analysis for the objective assessment of symptom severity in schizophrenia spectrum disorders (SSD), including negative symptom severity. However, the extent to which antipsychotic side effects influence speech markers of negative symptoms is unclear. This study investigated associations between computational speech and negative symptom severity, and examined whether speech features are associated with antipsychotic dose and extrapyramidal symptoms (EPS). Sixty-seven participants with SSD completed speech-focused tasks and clinical assessment. Seventeen speech features capturing relevant acoustic, timing, and linguistic characteristics were extracted from participant responses and examined for associations with negative symptoms, EPS, and antipsychotic dose. Eight timing features and one linguistic feature were significantly correlated with negative symptom severity (p < .05, false-discovery-rate-corrected; FDR), and these relationships were specific to negative symptoms rather than overall psychiatric symptom severity. No features were significantly correlated with antipsychotic dose or EPS after FDR correction, and Bayesian analyses provided moderate evidence for the null hypothesis (i.e., absence of an association) for most features. Two features showed Bayesian evidence for the alternative hypothesis, however; the association between speech and negative symptoms remained significant after removing the clinical covariate effects. The results provide support for computational speech markers of negative symptom severity, and both frequentist and Bayesian evidence that these speech markers are generally not confounded by antipsychotic side effects. The results help advance the clinical validation of computational speech assessment and analysis for measuring negative symptom severity in SSD.

Background: Changes to speech and language are common symptoms across different subtypes of frontotemporal dementia (FTD). These changes affect the ability to communicate, impacting everyday functions. Accurately assessing these changes may help clinicians to track disease progression and detect response to treatment. Objective: To determine which aspects of speech show significant change over time and to develop a novel composite score for tracking speech and language decline in individuals with FTD. Method: We recruited individuals with FTD to complete remote digital speech assessments based on a picture description task. Speech samples were analyzed to derive acoustic and linguistic measures of speech and language, which were tested for longitudinal change over the course of the study and were used to compute a novel composite score. Results: Thirty-six (16 F, 20 M; M_age = 61.3 years) individuals were enrolled in the study, with 27 completing a follow-up assessment 12 months later. We identified eight variables reflecting different aspects of language that showed longitudinal decline in the FTD clinical syndrome subtypes and developed a novel composite score based on these variables. The resulting composite score demonstrated a significant effect of change over time, high test–retest reliability, and a correlation with standard scores on various other speech tasks. Conclusion: Remote digital speech assessments have the potential to characterize speech and language abilities in individuals with FTD, reducing the burden of clinical assessments while providing a novel measure of speech and language abilities that is sensitive to disease and relevant to everyday function.

Objective: Depression is a common mental health disorder and a major public health concern, significantly interfering with the lives of those affected. The complex clinical presentation of depression complicates symptom assessments. Day-to-day fluctuations of depression symptoms within an individual bring an additional barrier, since infrequent testing may not reveal symptom fluctuation. Digital measures such as speech can facilitate daily objective symptom evaluation. Here, we evaluated the effectiveness of daily speech assessment in characterizing speech fluctuations in the context of depression symptoms, which can be completed remotely, at a low cost and with relatively low administrative resources. Methods: Community volunteers ( N = 16) completed a daily speech assessment, using the Winterlight Speech App, and Patient Health Questionnaire-9 (PHQ-9) for 30 consecutive business days. We calculated 230 acoustic and 290 linguistic features from individual's speech and investigated their relationship to depression symptoms at the intra-individual level through repeated measures analyses. Results: We observed that depression symptoms were linked to linguistic features, such as less frequent use of dominant and positive words. Greater depression symptomatology was also significantly correlated with acoustic features: reduced variability in speech intensity and increased jitter. Conclusions: Our findings support the feasibility of using acoustic and linguistic features as a measure of depression symptoms and propose daily speech assessment as a tool for better characterization of symptom fluctuations.

Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open‐ended speech samples from a prodromal‐to‐mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significant longitudinal change over 18 months. Nine acoustic and linguistic measures were combined to create a novel composite score. The speech composite exhibited significant correlations with primary and secondary clinical endpoints and a similar effect size for detecting longitudinal change. Our results demonstrate the feasibility of using automated speech processing to characterize longitudinal change in early AD. Speech‐based composite scores could be used to monitor change and detect response to treatment in future research.HIGHLIGHTS: Longitudinal speech samples were analyzed to characterize speech changes in early AD. Acoustic and linguistic measures showed significant change over 18 months. A novel speech composite score was computed to characterize longitudinal change. The speech composite correlated with primary and secondary trial endpoints. Automated speech analysis could facilitate remote, high frequency monitoring in AD.

Background: More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. Objective: This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. Methods: The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies’ ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. Results: Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. Conclusions: This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. International Registered Report Identifier (IRRID): DERR1-10.2196/35442

Background: Changes to speech and language patterns in Alzheimer’s disease (AD) may provide sensitive indicators of cognitive decline that could be automatically and objectively detected with speech analysis technology that incorporates natural language processing. In this study, we sought to determine whether analyses of audio recordings of open‐ended Clinical Dementia Rating (CDR) interviews collected as part of a Phase 2 clinical trial in prodromal‐to‐mild AD could identify speech and language characteristics relevant to clinical status.Method: Speech recordings from baseline CDR interviews were analyzed for 101 English‐speaking AD participants (58F/43M; 30 prodromal/71 mild; mean age = 69 years, range: 53‐80) in the semorinemab (NCT03289143) Phase 2 Tauriel study. Recordings of the CDR interview were analyzed using the Winterlight speech analysis platform, which generates >500 variables describing acoustic and linguistic features of speech. We computed Pearson correlations between the speech variables and five clinical measures (CDR, ADAS‐Cog, RBANS, MMSE, ADCS‐ADL) to identify specific features related to clinical performance.Result: CDR‐SB scores correlated more strongly with linguistic variables (e.g., types of words used, complexity of language) than acoustic variables. Participants with higher CDR‐SB scores (i.e., greater cognitive/functional impairment), used fewer past tense verbs when describing a recent event, a lower proportion of nouns compared to verbs, and shorter and simpler grammatical clauses (all r’s > 0.3, p’s < 0.05, FDR‐corrected). Similar associations were seen with other clinical assessments, and a subset of linguistic variables demonstrated significant (p < 0.05, uncorrected) correlations with all five clinical measures, including clause length, and use of prepositional phrases.Conclusion: These analyses demonstrate that clinical interviews can be analyzed using objective automatic methods to generate speech variables relevant to clinical outcomes in AD. Individuals with greater cognitive and functional impairment used simpler language and fewer content words when describing a recent experience. Future work will include multivariate analyses of speech markers versus cross‐sectional clinical indices.

Background: Speech and language changes have been reported to occur across a range of neurodegenerative disorders, including Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD) and Parkinson’s Disease (PD). Characterizing and quantifying such changes will enable the development of novel speech‐based measures to identify and monitor disease remotely and non‐invasively. In order to determine if such measures are disease‐specific, it is important to compare speech and language changes across different neurological conditions. In this study, we identify speech and language characteristics that are differentially affected across AD, FTD and PD populations.Method: In this cross‐study comparison, we pooled data from normative studies of older adults (N = 299), and studies of individuals with a clinical diagnosis of AD (N = 895), FTD (N = 43) or PD (N = 42). In all studies, speech was recorded as participants performed a picture description task, in which they were shown a line drawing of a scene and asked to describe everything they saw in the picture. Speech samples were transcribed and analyzed, producing >500 acoustic and linguistic variables describing the characteristics of the speech sound and content. Speech variables were compared across groups using ANOVAs with a factor of diagnosis group, and significant group effects (p < 0.05) were further examined with pairwise group comparisons.Result: Speech variables showing common or differential effects according to diagnosis were identified in this exploratory cross‐study comparison. Speech variables relating to the ease of speech production, including speech rate and number of pauses, were affected in all three diseases when compared to control participants. Select acoustic variables, including mean intensity and zero‐crossing rate, showed the greatest differences in PD compared to FTD or AD. Individuals with AD and FTD produced picture descriptions with less relevant information content. Select lexical variables, including pronoun and preposition use, were selectively affected in AD but not FTD or PD.Conclusion: This study indicates that speech and language characteristics, derived from a picture description task, were differentially affected in AD, FTD, and PD. We found evidence for acoustic changes in PD, consistent with motor speech impairments, and linguistic changes in AD and FTD, consistent with cognitive impairments.

Detecting early signs of cognitive decline is crucial for early detection and treatment of Alzheimer's Disease. Most of the current screening tools for Alzheimer's Disease represent a significant burden, requiring invasive procedures, or intensive and costly clinical testing. Recent findings have highlighted changes to speech and language patterns that occur in Alzheimer's Disease, and may be detectable prior to diagnosis. Automated tools to assess speech have been developed that can be used on a smartphone or tablet, from one's home, in under 10 min. In this study, we present the results of a study of older adults who completed a digital speech assessment task over a 6-month period. Participants were grouped according to those who scored above (N = 18) or below (N = 18) the recommended threshold for detecting cognitive impairment on the Montreal Cognitive Assessment (MoCA) and those with diagnoses of mild cognitive impairment (MCI) or early Alzheimer's Disease (AD) (N = 14). Older adults who scored above the MoCA threshold had better performance on speech composites reflecting language coherence, information richness, syntactic complexity, and word finding abilities. Those with MCI and AD showed more rapid decline in the coherence of language from baseline to 6-month follow-up, suggesting that this score may be useful both for detecting cognitive decline and monitoring change over time. This study demonstrates that automated speech assessments have potential as sensitive tools to detect early signs of cognitive impairment and monitor progression over time.